Trusted decision support system and method

ABSTRACT

Methods and apparatus for providing a comprehensive decision support system to include predictions, recommendations with consequences and optimal follow-up actions in specific situations are described. Data is obtained from multiple disparate data sources, depending on the information deemed necessary for the situation being modeled. The decision support system provides a prediction or predictions and a recommendation or a choice of recommendations based on the correlative analysis and/or other analyses. Also described are methods and apparatus for developing application specific decision support models. The decision support model development process may include identifying multiple disparate data sources for retrieval of related information, selection of classification variables to be retrieved from the data sources, assignment of weights to each classification variable, selecting and/or defining rules, and selecting and/or defining analysis functions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/874,493, filed on May 14, 2020, which is a continuation of U.S.application Ser. No. 15/265,551, filed on Sep. 14, 2016, which is acontinuation of U.S. application Ser. No. 13/970,514, filed on Aug. 19,2013, which is a continuation of U.S. application Ser. No. 13/399,249,filed on Feb. 17, 2012 and issued on Aug. 20, 2013 as U.S. Pat. No.8,515,895, which is a continuation of U.S. application Ser. No.12/431,656, filed on Apr. 28, 2009, which is a continuation of U.S.application Ser. No. 11/418,448, filed on May 3, 2006 and issued on Apr.28, 2009 as U.S. Pat. No. 7,526,455, which claims priority benefit ofU.S. Provisional Application No. 60/677,164 filed on May 3, 2005 and ofU.S. Provisional Application No. 60/735,539 filed on Nov. 10, 2005, allof which are incorporated by reference in their entireties for allpurposes.

Application Ser. No. 11/418,448 is one of a set of related U.S.applications filed May 3, 2006 including: application Ser. No.11/418,385 (now abandoned), Ser. No. 11/418,381 (now U.S. Pat. No.7,609,159), Ser. No. 11/418,380 (now U.S. Pat. No. 7,656,286), Ser. No.11/418,472 (now abandoned), Ser. No. 11/417,910 (now abandoned), Ser.No. 11/418,496 (now abandoned), Ser. No. 11/417,887 (now U.S. Pat. No.7,512,583), Ser. No. 11/418,448 (now U.S. Pat. No. 7,526,455), Ser. No.11/418,382 (now abandoned), Ser. Nos. 11/418,395, 11/418,447 (nowabandoned), and Ser. No. 11/417,893 (now abandoned). Each of the set isincorporated by reference in its entirety.

BACKGROUND Field

The field of the invention relates to complex data modeling and datamodeling. More particularly, the invention relates to massivecorrelative analysis and predictive modeling.

Description of the Related Art

The exponential increase of information over the last half-century iswidely reported yet the impact of this on decision making has gonelargely unnoticed. It's not that the decisions themselves have becomemore difficult—just that our expectations have become exponentiallyhigher as a result of the volume of available data, coupled with ouraccess to vast computer processing power.

As most any modern decision-maker can attest, this volume of informationsurrounding decisions is not always helpful. In all but very few cases,one must rely upon myriad disparate sources of information, each havingbeen gathered and structured in its own idiosyncratic way. This causesseveral fundamental problems: 1) the information is often collected orinput by inadequately trained individuals who don't understand theimportance of consistent, quality data; 2) each database is designed toserve a particular purpose and rarely lends itself to use outside thenarrow scope of its original purpose (e.g. comparing apples to oranges);and 3) most data collected is not accurately synchronized with atime/space context that all but prevents accurate cross-referencing withsimilar information.

For decades researchers have been attempting to address this growingproblem. Solutions have been labeled everything from Recommender Systemsto Artificial Intelligence, but met with only modest results. Thisdisappointing outcome can be attributed partially to the fundamentaldata weaknesses outlined above and partially to the myopic scope ofthese earlier solutions. A growing body of research in complex systemstheory suggests that many phenomena are significantly impacted by alarge number of adjacent spheres of influence—an observations referredto as “small world networks”. In field after field, researchers arediscovering a high degree of interconnectivity that reveals newcorrelations never before understood. This has led some to draw aparallel to a similar phenomenon in the social domain called “Sixdegrees of separation”. This work has recently been successfullyverified in applications ranging from protein-protein interactions in acell to the behavior of a terrorist cell. In all these cases, the commontheme is the behavior of each component depends on the behavior ofothers. Thus there is a need for improved systems and methods for makingdecisions and for carrying out related tasks

SUMMARY

The system, method, and devices of the invention each have severalaspects, no single one of which is solely responsible for its desirableattributes. Without limiting the scope of this invention, its moreprominent features will now be discussed briefly. After considering thisdiscussion, and particularly after reading the section entitled“Detailed Description of Certain Embodiments” one will understand howthe features of this invention provide advantages over other errormanagement solutions.

One embodiment is a system comprising an electronic device configured toselect a data set for an application area, to assign weighted scores tothe data in the data set, to correlate the weighted data set with one ormore previously correlated weighted data sets, and to determine, basedupon the correlation, a recommended action as a response to an eventrelated to the data set.

One embodiment is a system comprising a sensor network, a databasecomprising data from the sensor network, and a electronic deviceconfigured to select a data set from the database for an applicationarea, to assign weighted scores to the data in the data set, tocorrelate the weighted data set with one or more previously correlatedweighted data sets, and to determine, based upon the correlation, arecommended action as a response to an event related to the data set.

One embodiment is a system comprising an electronic device configured toselect a data set for an application area, the electronic device furtherconfigured to analyze the data set according to fuzzy logic instructionsso as to generate a recommended action and outcome information for therecommended action.

One embodiment is a system comprising an electronic device configured toselect a data set for an application area, the electronic device furtherconfigured to analyze the data set according to statistical analysisinstructions so as to generate a recommended action and outcomeinformation for the recommended action.

One embodiment is a data analysis system, the system comprising adatabase comprising previously received data from a plurality ofsources, and an electronic device configured to receive current datafrom at least one of the plurality of sources, to compare the currentdata and the previously received data, and to provide a recommendedaction and outcome information for the recommended action based at leastin part on the comparison.

One embodiment is a data analysis system, the system comprising adatabase comprising previously received data from a plurality ofsources, and an electronic device configured to receive current datafrom at least one of the plurality of sources, to analyze the currentdata, and to predict an outcome based at least in part on the previousdata and the current data.

One embodiment is a data analysis system, the system comprising adatabase comprising previously received data from a plurality ofsources, and an electronic device configured to receive current datafrom at least one of the plurality of sources, to compare the currentdata and the previously received data, and to predict a future outcomebased at least in part on the determination.

One embodiment is a recommended action system comprising a database ofpreviously received data, a database of previously recommended actionsassociated with the previously received data, and an electronic deviceconfigured to receive current data, correlate the current data withpreviously received data, and to generate one or more recommendedactions based at least in part on the received data, the correlation,and the previously recommended actions.

One embodiment is a prediction system comprising a database ofpreviously received data, a database of previously predicted outcomesassociated with the previously received data, and an electronic deviceconfigured to receive current data, to correlate the current data withpreviously received data, and to predict one or more outcomes based atleast in part on the received data, the correlation, and the previouslypredicted outcomes.

One embodiment is a recommended action system comprising a database ofpreviously received data, a database of previous actual outcomesassociated with the previously received data, and an electronic deviceconfigured to receive current data, correlate the current data withpreviously received data, and to generate one or more recommendedactions based at least in part on the received data, the correlation,and the previous actual outcomes.

One embodiment is an electronic warrant or subpoena system, comprising adatabase of sensitive data comprising at least one of a plurality ofprivate records and a plurality of security records, and an electronicdevice configured to provide information from the sensitive data basedat least in part on a received electronic warrant or electronicsubpoena.

One embodiment is an electronic warrant or subpoena system, comprising aplurality of disparate data sources comprising a plurality of privatedata, and an electronic device configured to provide information fromthe data sources based at least in part on a received electronic warrantor electronic subpoena.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a network for implementing a trusted decision supportsystem.

FIG. 2 is a block diagram illustrating certain functional blocks of aserver based system for hosting the trusted decision support system.

FIG. 3 is a flowchart illustrating certain steps in a process fordeveloping an application specific decision support model.

FIG. 4 is a flowchart illustrating certain steps in a process forperforming a decision support process using an application specificmodel developed with the process of FIG. 4 .

FIG. 5 is a block diagram illustrating certain disparate data sourcesaccessible through a communication infrastructure for performing adecision support process related to an anti-terrorism application.

FIG. 6 is a block diagram illustrating certain disparate data sourcesaccessible through a communication infrastructure for performing adecision support process related to a general data types application.

FIG. 7 is a block diagram illustrating certain disparate data sourcesaccessible through a communication infrastructure for performing adecision support analysis related to a human factors application.

FIG. 8 is a block diagram illustrating certain disparate data sourcesaccessible through a communication infrastructure for performing adecision support analysis related to an ecosystem application.

FIG. 9 is a flowchart illustrating certain steps in a process forparsing private information and storing it in segregated databases,where the databases can be accessed in an anonymous mode to retainpersonal privacy, or accessed in an authorized mode that reconstructsthe private information including the personal identity.

FIG. 10 is a flowchart illustrating certain steps in a process forobtaining information for use in a decision support process.

FIG. 11 is an example template that can be created for an applicationwith the process of FIG. 3 .

DETAILED DESCRIPTION

Methods and apparatus for providing a comprehensive decision supportsystem to include predictions, recommendations with consequences andoptimal follow-up actions in specific situations are described. Data canbe obtained from multiple disparate data sources, depending on theinformation deemed necessary for the situation being modeled. Someembodiments perform complex systems modeling including performingmassive correlative analyses of the data obtained from the multipledisparate data sources with current situational data obtained regardingthe situation for which the decision support process is being utilized.The decision support system may provide a prediction or predictions anda recommendation or a choice of recommendations based on the correlativeanalysis and/or other analyses. In some embodiments the decision supportsystem may provide possible consequences that could result from arecommendation. In other embodiments the decision support system mayprovide a list of tasks for acting upon a recommendation. Also describedare methods and apparatus for developing application specific decisionsupport models. The decision support model development process mayinclude identifying multiple disparate data sources for retrieval ofrelated information, selection of classification variables to beretrieved from the data sources, assignment of weights to eachclassification variable, selecting and/or defining rules, and selectingand/or defining analysis functions.

In the following description, specific details are given to provide athorough understanding of the disclosed methods and apparatus. However,it will be understood by one of ordinary skill in the art that thedisclosed methods and apparatus may be practiced without these specificdetails. For example, electrical components may be shown in blockdiagrams in order not to obscure certain aspects in unnecessary detail.In other instances, such components, other structures and techniques maybe shown in detail to further explain certain aspects.

It is also noted that certain aspects may be described as a process,which is depicted as a flowchart, a flow diagram, a structure diagram,or a block diagram. Although a flowchart may describe the operations asa sequential process, many of the operations can be performed inparallel or concurrently and the process can be repeated. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

FIG. 1 shows a network for implementing a trusted decision supportsystem. The decision support system 100 includes a server 105, a storagecomponent 110, client terminals 115A, 115B and 115C, where three clientterminals is only used as an example, and a network 120 connecting theother components.

The server 105 contains processing components and software and/orhardware components for implementing the decision support system. Theserver 105 contains a processor for performing the related tasks of thedecision support system. The server 105 also contains internal memoryfor performing the necessary processing tasks. In addition, the server105 is connected to an external storage component 110 via the network120. The processor is configured to execute one or more softwareapplications to control the operation of the various modules of theserver as will be discussed below in reference to FIG. 2 . The processoris also configured to access the internal memory of the server 105, orthe external storage 110 to read and/or store data. The processor may beany conventional general purpose single- or multi-chip microprocessorsuch as a Pentium® processor, Pentium II® processor, Pentium III®processor, Pentium IV® processor, Pentium® Pro processor, a 8051processor, a MIPS® processor, a Power PC® processor, or an ALPHA®processor. In addition, the microprocessor 100 may be any conventionalspecial purpose microprocessor such as a digital signal processor.

The storage component 110 contains memory for storing information usedfor performing the decision support processes provided by the system100. Memory refers to electronic circuitry that allows information,typically computer data, to be stored and retrieved. Memory can refer toexternal devices or systems, for example, disk drives or tape drives.Memory can also refer to fast semiconductor storage (chips), forexample, Random Access Memory (RAM) or various forms of Read Only Memory(ROM), that are directly connected to the processor. Other types ofmemory include bubble memory and core memory.

The client devices 115A-115C represent any type of device that canaccess a computer network. Devices such as PDA's (personal digitalassistants), cell phones, personal computers, lap top computers, set topboxes are examples of devices that could be used as the client devices115. The client devices will typically have a display device and one ormore input devices. For example, the input device may be a keyboard,rollerball, pen and stylus, mouse, or voice recognition system. Theinput device may also be a touch screen associated with an outputdevice. The user may respond to prompts on the display by touching thescreen. Textual or graphic information may be entered by the userthrough the input device.

The network 120 may include any type of electronically connected groupof computers including, for instance, the following networks: Internet,Intranet, Local Area Networks (LAN) or Wide Area Networks (WAN). Inaddition, the connectivity to the network may be, for example, remotemodem, Ethernet (IEEE 802.3), Token Ring (IEEE 802.5), Fiber DistributedDatalink Interface (FDDI) or Asynchronous Transfer Mode (ATM). Note thatcomputing devices may be desktop, server, portable, hand-held, set-top,or any other desired type of configuration. As used herein, the networkincludes network variations such as the public Internet, a privatenetwork within the Internet, a secure network within the Internet, aprivate network, a public network, a value-added network, an intranet,and the like.

FIG. 2 is a block diagram illustrating certain functional blocks of aserver based system for hosting the trusted decision support system. Thesystem 200 includes the server 105 that is connected, to anadministrative workstation 236, and to external data sources 240. Theserver 105 is comprised of various modules 202-234. As can beappreciated by one of ordinary skill in the art, each of the modules202-234 comprises various sub-routines, procedures, definitionalstatements, and macros. Each of the modules 202-234 are typicallyseparately compiled and linked into a single executable program.Therefore, the following description of each of the modules 202-210 isused for convenience to describe the functionality of the server 105.Thus, the processes that are undergone by each of the modules 202-234may be arbitrarily redistributed to one of the other modules, combinedtogether in a single module, or made available in a shareable dynamiclink library. Further each of the modules 202-234 could be implementedin hardware.

The controller 220 serves as the central interface linking the othermodules 222-234. The controller 220 coordinates the use of the othermodules based on the script instructions that define the task (e.g., anapplication specific decision support model) that is being processed.The modules 232-234 execute the various functions used to perform thedecision support processes. The data modules 202-210 comprise memorythat stores previously generated data and is also used to store new dataas it is generated by the other modules. The data storage interfacemodule 224 is configured to retrieve and store data to and from the datamodules 202-210.

The real-time monitoring interface 222 is configured to monitor multipledisparate data sources in search of information that matches any ofseveral variables that the current decision support model is designed toretrieve. In some embodiments, the real-time monitoring interface 222retrieves from the disparate data sources that are part of a securecommunications network for use with the decision support system. Inthese embodiments, each of the disparate data sources conforms to asecurity protocol and a data format. The data that is retrieved by thereal-time monitoring interface 222 is forwarded to the controller 220which then forwards the data for use by other modules or to be stored.

Disparate data sources that are part of a secure communication networkmay include data sources that are under the control of an organizationthat controls the communications network that the data is retrievedfrom. For example, if the decision support system is part of a U.S.government communications network, the following databases may bedirectly connected to the decision support system and may conform to thesecurity protocol and data format:

-   -   FBI's INFRAGUARD database    -   National Homeland Security database    -   FAA Airmen Certification database    -   FAA Registered Aircraft database    -   SEC filings EDGAR database    -   TECS US Customs Enforcement database

The real-time monitoring interface 222 retrieves data from a set ofconforming data sources. In contrast, an import data translation module226 is configured to retrieve information from non-conforming datasources 240. The information may be any that matches any of the severalvariables that the current decision support model is designed toretrieve. The non-conforming data sources can be accessed from any ofseveral public, private, secure, and/or non-secure networks such asthose discussed above in reference to the network 120 of FIG. 1 .Non-conforming data sources may include web pages on the Internet. TheInternet contains a plurality of web pages that are searchable by theimport data translation module 226. The web pages are virtual documentsthat each have embedded links which link portions of the virtual pagesto other virtual pages and other data. Other examples of data sourcesthat may be accessed using the real-time monitoring interface includeprivate databases such as Lexis/Nexis, Dow Jones, Medline, etc, each ofwhich have different security, quality, structure and subject matter.The import data translation module 226 can traverse the virtual pagesand download data that matches any of the several variables that thecurrent decision support model is designed to retrieve. The data that isretrieved by the import data translation module is converted to conformto the same data format that the real-time monitoring interface module222 uses. The conversion to the conforming format may includeclassifying portions of the information into the various searchvariables and then parsing the classified information variables intospecific fields of a multidimensional data structure (e.g., fields mayinclude time, location, account numbers, flight numbers, etc.). Afterthe retrieved data is converted to the conforming format, it isforwarded to the controller 220 for use in other modules or to bestored.

In some embodiments, the data that is retrieved by the import datatranslation component 226 may be of questionable integrity. Networkssuch as the internet typically employ a base level of protection fortransporting data of all kinds. One example is the Transport ControlProtocol (TCP). TCP is a transport layer protocol used to provide areliable, connection-oriented, transport layer link among computersystems. The network layer provides services to the transport layer.Using a two-way handshaking scheme, TCP provides the mechanism forestablishing, maintaining, and terminating logical connections amongcomputer systems. TCP transport layer uses IP as its network layerprotocol. Additionally, TCP performs functions such as transmission ofbyte streams, data flow definitions, data acknowledgments, lost orcorrupt data re-transmissions, and multiplexing multiple connectionsthrough a single network connection. Finally, TCP is responsible forencapsulating information into a datagram structure. An integrityservices module 242 is configured to perform the integrity checks of TCPor other types of transport layer protocols known to those of skill inthe art (e.g., hypertext transmission protocol, secure: HTTPS). Theintegrity services module 242 may also be used to authenticatecontextual information obtained from non-conforming sources by comparingthe non-conformal source information to duplicate information that hasbeen obtained from trusted sources (e.g., sources such as the conformalsources connected to the real-time monitoring interface 222). This typeof contextual authentication may avoid the importation of accidentallyincorrect information and/or purposefully false information (e.g.,information that may have been planted for purposes of disinformation).

In some embodiments, the import data translation component 226 may usemethods to convert non-textual information into text to be stored. Manysources of information, especially on the internet, are stored invarious forms of multimedia including audio, image, and our videoformats. Various methods can be used to convert from a media format to atextual format. These methods include, for example, speech to textmethods, voice recognition methods, and image recognition (recognizingfaces, and/or objects and classifying them as identified people orthings). These types of conversions can be used to classify variables(e.g., time, location, people, and/or objects) discovered in audiofiles, image files and/or video files, and store them in the variousdatabases, such as the historical data module 202, for later analysis.

The disparate data sources, whether they are of the conforming type orthe non-conforming type, preferable come from as many different types ofnetworks and/or systems as possible. The larger the number of disparatedata sources, the more chance of that analysis tools such as the complexsystems modeling tools and massive correlative analysis tools mayuncover correlated events. In some embodiments, the information obtainedby the real-time monitoring interface 222 is data sampled by one or moreremote monitoring systems and/or remote sensors as described in U.S.application Ser. No. 11/418,385, filed May 3, 2006, entitled “Trustedmonitoring system and method.” Examples of disparate data sources thatmay be utilized for several application specific situations areillustrated in FIGS. 5 to 8 .

The modules 228, 232 and 234 are analysis components that are used insearching for patterns, correlated events, recommended actions and/orprobable consequences using various mathematical methods. A fuzzyinference engine component 228 is configured to analyze any of the newlyretrieved and/or stored data according to fuzzy logic rules defined bythe specific decision support model that is being executed. Examples ofcommercially available fuzzy inference engines include: mbFuzzIT,Mentalogic Systems Inc Fuzzy Inference Engine and the Fuzzy InferenceDevelopment Environment from Zaptron. Fuzzy logic can be used torepresent the correctness of a chain of reasoning connecting a chain ofevents. The correctness can vary from true (represented by a 1, forexample), to false (represented by zero, for example) and may be equalto intermediate values (almost true or almost false). Those of skill inthe art will recognize these and other uses of fuzzy logic. Fuzzy logicrules may be used to identify various inferences and/or implicationscorrelated to various chains of events. The various inferences and/orimplications may be the recommended actions and/or consequencesidentified by the decision support model.

A data mining module 232 is configured to analyze data retrieved by thevarious other modules using tools which look for trends or anomalieswithout knowledge of the meaning of the data. The data mining module 232is used to discover patterns and correlations in the large preexistingdata modules 202-210 in order to uncover new meaning in data. Examplesof commercially available data mining tools include: DataCruncher byDataMind, Darwin by Thinking Machines, and Intelligent Miner from IBM.

A statistical analysis engine 234 is configured to perform statisticalanalysis for determining probabilities, for example, for use indetermining the recommended actions generated by the decision supportsystem. Examples of commercially available statistical analysis toolsinclude SAS/STAT from SAS, MatLAB from MathWorks, and MS Office-Excelfrom Microsoft.

A graphic interface generation module 230 is configured to present agraphical user interface to a user of the administrative workstation 236in this example. The graphical user interface enables an administrativeuser to develop models and use the models for performing the decisionsupport analysis. Examples of commercially available graphical userinterface design tools that can be used to generate the graphical userinterfaces include CVSgui from WinCVS, System Management Interface Toolfrom IBM, and Visual Basic from Microsoft.

It should be noted that any one of the functional blocks illustrated inFIG. 2 may be rearranged and/or combined with one or more other blockswithout changing the function of the server 200.

FIG. 3 is a flowchart illustrating certain steps in a process fordeveloping an application specific decision support model. The process400 is used to develop a decision support model directed to a specificapplication for providing guidance and decision making to a user. Anapplication is a scenario that the decision support process is directedat. Examples of applications include the following application areaseach listing representative samples of related sub-applicationcomponents:

-   -   1. Flight safety        -   a. Vehicle integrity        -   b. Passenger integrity        -   c. Route integrity        -   d. Schedule integrity    -   2. Cargo logistics        -   a. Supplier/manufacturer integrity        -   b. Container integrity        -   c. Product integrity        -   d. Shipper integrity        -   e. Handling personnel integrity        -   f. Route integrity    -   3. Crude oil futures        -   a. Untapped reserves        -   b. Refining capacity        -   c. GNP of industrialized nations        -   d. Annual vehicle sales        -   e. News stories about oil        -   f. Annual production of petroleum based products        -   g. Financial markets commodity trading volume    -   4. Financial condition of public companies        -   a. Board integrity        -   b. Management integrity        -   c. Market integrity        -   d. Raw material integrity        -   e. Stock trading volume        -   f. Financial performance        -   g. Sector comparables        -   h. News reports        -   i. SEC filings notations        -   j. Legal actions pending

The process 400 starts at step 402 where a template of an applicationmodel is imported to the design process 400 from a library 422containing one or more templates of models that can be fine-tuned to fita given application (e.g., flight safety in this example). FIG. 11 is anexample template that can be created for an application by the process400. FIG. 11 is a completed template, whereas the templates that areimported at the step 402 are typically blank. A model identificationdata structure 1105 contains information input by the user to identifythe application model being designed. In this example, the model is anaviation safety model. The templates may contain a basic starting pointthat allows a user to select from multiple selections at each step, fromstep 404 to step 416 in this example, of the design process 400. In someembodiments, the templates may each be aimed at a central theme orcategory that the application areas (see step 404) are centered around.For example, one template may focus around business, another aroundentertainment and yet another around health.

After a template is imported, the design process 400 continues at step404 with the selection of the application area of interest. The exampletemplate 1100 contains an application data structure 1110 where theapplication area Civil Aviation is listed. In one embodiment, anapplication library 424 is a database containing previously identifiedapplication areas that will help narrow the design process 400 insubsequent steps. The application area may be a smaller subset of thetemplate category. For example, if the template is aimed at the businesscategory, then the application area may be business ethics, stock marketresearch, real estate investment, and venture capital funding. Theselection of the application area may be done in a tiered approach wherethe entire category is defined as a sort of tree structure with severalbranches.

Process 400 continues at step 406 with the identification of thedatabase (or simply data) sources. A database library 426 contains alist of disparate data sources that may be utilized. The template 1100lists three exemplary data sources that could be chosen for the aviationsafety application of the example. The data sources in data structure1115 include an aircraft data source, an air traffic route data source,and airline maintenance data sources. The database library is preferablyan evolving library where new data sources are added whenever they areuncovered in searches of other databases (e.g., new links to associatedweb pages may be identified during the process of running other relatedor unrelated decision support models). As more and more data sourcesbecome available, the more powerful the resulting decision supportsystem potentially becomes. Preferably, as many data sources as possibleare identified at step 406. However, constrains such as time, processingpower, security clearance and others may limit the databases that a userselects to include in the application specific model. In someembodiments, the data sources may be categorized into pre-selectedcategories that are already match to the application area and/ortemplate that the user has selected. The pre-selected data sources maybe the result of past success in obtaining relevant information duringthe execution of other decision support models in the same or similarapplication areas.

FIGS. 5-8 illustrate examples of disparate data sources that may be usedin designing a decision support system for four specific applicationareas FIG. 5 is a block diagram illustrating certain disparate datasources accessible through a communication infrastructure for performinga decision support process related to an anti-terrorism application.FIG. 6 is a block diagram illustrating certain disparate data sourcesaccessible through a communication infrastructure for performing adecision support process related to a general data types application.FIG. 7 is a block diagram illustrating certain disparate data sourcesaccessible through a communication infrastructure for performing adecision support analysis related to a human factors application. FIG. 8is a block diagram illustrating certain disparate data sourcesaccessible through a communication infrastructure for performing adecision support analysis related to an ecosystem application. The FIGS.5-8 are simply examples of the types of different data sources that canbe selected at step 406 when using the process 400.

Process 400 continues at step 408 with the selection of the variables tobe searched for at each source. Some embodiments may search for the samevariables on all data sources while other embodiments may specify datasource specific variables to be searched. The template 1100 contains avariable data structure 1120. The variables in the data structure 1120include a vehicle registration number variable, a vehicle model variableand a vehicle payload variable. These are example variable and are notmeant to be an exhaustive list.

The variables define the information elements that will be searched forduring the decision support process. For example, if the applicationarea is airline flight safety, some of the variables may include airlinenames, passenger lists, airline accident records, flight numbers,weather, time of year, destination and origination points and manyothers. A variable library 428 contains information that may be used inselecting the variables at step 408. In some embodiments, the variablelibrary may be an evolving library which includes variables selected byother users when designing their models. The variable library 428 maycontain variables categorized under various headings in a tieredhierarchy. For example, a category of personal identification mayinclude variables listed for drivers license variables, birthcertificate variables, international visa variables, and or passportvariables. The passport variables might contain the following choices:

-   -   Passport number    -   Photograph    -   First name    -   Last name    -   Birth date    -   Street address, state, zip    -   Issue date    -   Issue location    -   Valid until date

After the variables are selected, the process 400 continues at step 410where weights are assigned to each variable. The weights are used incalculated the relevance of information found at the many disparatesources that were selected at step 406. Higher weights should be givento those variables that are most important to the decision supportprocess. The template 1100 lists the weights assigned to each variablein the variables data structure 1120. A weighting guidelines library 430contains suggested weightings for variables. The weighting guidelinesmay be based on the application area, the database source, and othercriteria. In some embodiments, the weighting guidelines evolve ordevelop over time in a training session. The training session may be inthe design phase of the model. The training session may continue duringactual use by multiple clients. This way the weighting guidelines changeas the information and world of events changes.

Time frame limits and location limitations may be defined at step 412.Each application may look at the time variable differently. For examplein a flight safety application, the time range may be fairly small withregard to reaching pre-defined waypoints or performing certain aircraftnavigation duties. In contrast, the status of a shipping container maybe of low interest for weeks at a time but once the vessel comes withina certain range of port, the relevant timeframe may change to minutes.Lastly, the relevant timeframe for a valid passport may be ten yearswhereas a visa may only be valid for days or weeks. Time frame limitsmay be added to narrow the searches for predicted outcomes and/orcorrelated past events to a certain range around the current time thatthe model is being run. This may be desired in order to save time and/orprocessing power. The example template 1100 contains a time frame andlocation limitations data structure 1125 listing some exemplary uses oftime and location limitations. In the example template 1100, thesearches will be limited to Persons over 18 and present in the US asrelevant to any analysis. However, with respect to Aircraft, the searchis broadened to include any craft globally with the same model number.Finally, the search will consider any route flown from 1940 to thepresent in the U.S. to be of relevance. A timeframe guideline library432 contains suggested time frame limitations for each data type andapplication area.

Fuzzy rules are selected or defined at step 414. Fuzzy rules may includechains of fuzzy variable membership functions combined with fuzzy logicoperations including unions, intersections, and complements orcombinations thereof. Fuzzy rules may also include modifiers (e.g.,raising membership functions to a power to add a degree of correctnessor incorrectness) that may be used to apply the weighting factorsassigned in step 410. The template 1100 contains a rules definition datastructure 1130 that lists rules sets (identified by rule numbers) foreach variable and lists the associated Fuzzy Rules System and/orApplication that the rule set numbers relate to. In this example, a RuleLibrary 434 presents a list of tools, each of which is linked to aseries of pre-defined rule sets is used In the first instance, anapplication Zaptron FIDE that resides on system SuperSam3 is selectedand Rule set 87 is used to identify anomalies in the FAA registrationdatabase. The rules selection process is continued for each variable orvariables with as many rules as needed. A fuzzy rule library may containguidelines to designing the fuzzy rules in step 414. In someembodiments, the fuzzy rule guidelines contained in the library 434evolve or develop over time (e.g., during development of the system orduring actual use of the system). The basics of fuzzy logic are known tothose of skill in the art and will not be discussed in detail.

Similar to fuzzy logic base rules, analysis functions may be designed ordefined at step 416. Analysis functions can include probability theory,Boolean logic, data mining and other types of correlative types ofanalysis functions. Analysis functions can include any of severalanalysis techniques known to those of skill in the art, such as:

-   -   Probability distribution for random variable X    -   Sampling distribution of the mean    -   Confidence interval estimation of the mean for (variable X)    -   Z test of hypothesis for the mean    -   t Test for the mean difference between (variable X and variable        Y)    -   Autoregressive modeling for trend fitting and forecasting    -   Residual analysis for the multiple regression

The template 1100 contains an analytical process data structure 1135that lists analytical processes selected for each variable and lists thespecific analytical techniques and Systems and/or Applications to beused. In the example template 1100, a Function Library 436 presents alist of tools, each of which is linked to a series of pre-definedanalytical analysis sets. In the first instance, a MatLAB7 tool thatresides on system BigBlue7 comprising Bayes Function set 45A is selectedto identify anomalies in the passenger manifest. The analysis functionselection process is continued for each desired variable or variableswith as many rules as needed. A function library, 436 contains suggestedanalysis functions. In some embodiments, the analysis guidelinescontained in the library 436 evolve or develop over time (e.g., duringdevelopment of the system or during actual use of the system).

At the completion of the design process 400, the application specificmodel (or script) is stored at step 418. The model can be stored in themodel library 422 that also stored the template that was used to startthe process 400. The model can be loaded from memory to be used by thedesigner or by other clients depending on the embodiments. It should benoted that any of the steps and/or libraries in FIG. 3 may be rearrangedor combined with one or more other steps without changing the functionof the process 400.

FIG. 4 is a flowchart illustrating certain steps in a process forperforming a decision support process using an application specificmodel developed with the process of FIG. 3 . Process 500 begins byimporting an already designed decision support model, created using theprocess 400, from a model library 532. The model library 532 can be thesame as the model library 422 of FIG. 3 . The controller 220 of FIG. 2can perform the importing of the application model. The controller 220can also coordinate the flow of the process 500 to the other componentsof the server 200 that can be conducting some or all of the process 500steps.

After importing the application model, the process 500 continues to step504 where a correlation process is conducted. Typically, a currentscenario 505 of the applicable variables is input to the process 500 inorder to perform the correlation acts. The current scenario 505 caninclude values for each of the selected variables designed into thedecision support model being executed (e.g., the variables selected atstep 408 in the process 400). The current scenario 505 variables can beupdated periodically using the real-time monitoring interface 222 and/orthe external/non-conforming data sources component 240 of FIG. 2 .

During the correlation process, the current scenario is correlated withpreviously obtained data contained in the various libraries/storagecomponents such as, for example, a scenario library 534 and/or thehistorical data module 202 shown in FIG. 2 . The data storage interfacecan retrieve the previously obtained data contained in the librariesand/or storage components. The correlation process preferably uses theweights assigned to each variable, at step 410 in the model designprocess 400, in calculating the correlations. This way the correlatedhistorical scenarios will be skewed in the direction of the moreimportant variables. Some models can be conducted without weights, orwith all weights set to a default value such as one. “Complex SystemsModeling” (e.g., data mining) may be used to identify the correlationsbetween the current scenario data obtained from the multiple disparatedata sources and the historical data stored in the scenario library 534.The data mining components 232 of FIG. 2 can be used in performing thecorrelation tasks of step 504, e.g., using pattern recognitiontechniques.

In addition, or instead of, the correlative analysis performed at step504, the process 500 can also conduct fuzzy logic rule processing atstep 506 in identifying previously obtained chains of events that mayrelate to the current scenario 505. The fuzzy rules may be stored in arule library 536. The fuzzy logic rules can enable the process 500 toidentify inferences and/or implications in the historical chains ofevents and compare these chains of events to the current chain of eventsas exhibited by the current scenario 505. The fuzzy rules can bedesigned to reflect the variable weights assigned to the variables instep 410 of the process 400. The fuzzy inference engine 228 can conductthe fuzzy rule processing of step 506.

After identifying historical scenarios that correlate to or areindicated via fuzzy logic rules to relate to the current scenario chainof events, multiple analyses are conducted at step 508. Analyses caninclude statistical analyses, Bayesian analyses, and/or neural networkanalyses. A function library 538 contains stored analytical functionsused at step 508. In some embodiments, the current scenario data can beanalyzed to identify parameters or events that are outside of acceptablenormal ranges. The current scenario variables can be compared to theaccepted ranges (e.g., historical ranges, calculated ranges etc.) inorder to identify outlying characteristics. The normal ranges for thevariables in question can be stored in the historical ranges component204 shown in FIG. 2 . In addition to the accepted ranges method ofidentifying possible non-normal conditions, an expert informationdatabase, such as the expert info component 206 of FIG. 2 , can be usedto identify states that are judged to be unusual by experts of fieldsthat are associated with the application model being executed in thecurrent process. In some embodiments, the expert information databasemay indicate subjective data values corresponding to states such as“optimal”, “danger”, “recommended action’ or other states, that areindicative of the current scenario chain of events 505. These are someexamples of the multiple analyses that can be conducted at step 508.Other forms of analyses known to those of skill in the art may also beconducted. The statistical analysis component 234 of FIG. 2 can performthe analyses at step 508.

Based on the multiple analyses performed at step 508, the processproceeds to step 510 where predictions are made as to what the nextlikely outcome or event will be. The multiple predictions can be made tofit a time limit determined by the step 412 of the process 400 where thetime frame to be used in the application specific model was defined. Themultiple analyses performed at step 508 can form the basis forpredicting the most likely outcomes. In some embodiments, the mostprobable outcomes identified by the statistical, neural network, and/orfuzzy logic analyses of step 508 are used as the predictions at step510. A prediction library 540 contains [templates that specify one ormore parameters and values, one or more formulae, and one or morepredictions at specified time points along a projected vector. Forexample, in flight safety a historical template might read as follows:[PARAMETERS] SPEED: 600 mph; PITCH: 5 degrees; ALTITUDE: 5,000 FT[PREDICTION] EVENT: Imminent Crash; TIMEFRAME: 3 minutes, 20 seconds. Insome embodiments, the severity of the outcome is contained in theprediction library 540. The severity of the outcome can be a subjectivetype of measure indicating the extremeness of an outcome, whetherpositive or negative. For example, an extremely positive outcome couldbe that the prediction analysis indicates that the user will win thelottery. An extremely negative outcome could be that the user can expectto lose 90% of his investments in the next year. The extremeness of theoutcome can be considered when the recommended actions are determined atstep 512 and the potential consequences determined at step 514, asdiscussed below. The statistical analysis component 234, the fuzzyinference engine 228 and/or information retrieved by the controller 220(using the data storage interface 224) can perform the prediction actsof step 510.

At step 512, recommendations are identified based, at least in part, ontheir effect on the predicted outcome derived at step 510. Pastscenarios that have been identified by the correlation step 504 and orthe fuzzy rule processing step 506 can form the basis for making therecommended actions. The recommended actions may be a single action ormultiple actions. The recommended actions may include doing nothing. Therecommended actions may be made for the range of time selected at step412 of the process 400. Preferably, the recommended actions are made inparallel with determining the potential outcomes or consequences at step514. A recommendation library 542 contains a historical record of pastrecommended actions and subsequent results that are used to affect therecommendations. These historical recommended actions can be identifiedby links that were identified in the correlation process of step 504.Historical recommendations that resulted in both positive and negativeoutcomes can be included, preferably with negative outcomes causing achange in the historical recommendation and a positive outcomereinforcing the historical recommendation. The controller 220 can locateand retrieve the historical recommendations from the recommendationactions module 210 shown n FIG. 2 . Using these historicalrecommendations, the controller 220 can make the one or morerecommendations at step 512.

As discussed above in relation to making predictions at step 510 andmaking recommendations at step 512, the potential consequences as wellas the severity of the consequences are determined at step 514(preferable steps 510, 512 and 514 are executed simultaneously such thatthe severity of the predicted outcomes affect the recommended actins).The consequences are stored in a consequence library 544. Theconsequences contained in the library 544 are linked to the severitymeasures discussed above in relation to making the predictions at step510.

After determining the potential consequences (outcomes) at step 518, therecommended action/consequence results 518 are presented to the user inorder to receive user input 520 as to which recommended action to take.The user can be presented the actions/consequences on the display of theclient device (e.g., any of client devices 115 shown in FIG. 1 ). Insome embodiments the user is given a choice to select automatic ormanual selection of the recommended actions. If the user selects theautomatic selection option, a decision block 516 will detect thisselection and the process 500 will proceed to step 522 where therecommended action or actions are performed. In some embodiments, theuser may be presented with a list of one or more historical scenariosincluding the recommended actions that were given, the tasks that wereperformed to carry out the recommended actions and the resultingoutcomes. These historical recommended actions may not correspond to thecurrent recommended actions, but are presented to the user so as to letthe user observe what outcomes may lay ahead if he were not to performthe recommended actions.

The automatic selection of actions may choose the action(s) that resultin a highest risk reward measurement. The risk reward measurement mayqualitative measure (e.g., most happy, least injured, etc.) or aquantitative measure (e.g., highest rate of return on investment, lowestmortgage rate, etc.). The automatic selection may, in some embodimentschoose the action that results in optimizing a user specified condition,such as, for example, fastest trip to a destination, lowest grocery billsatisfying nutritional needs and others.

If the user selects the manual selection of the recommended actions,then the user also inputs (user input 220) the recommended action. Thedecision block 516 then causes the process 500 to proceed to step 524where the manual action is performed. It should be noted that therecommended actions made at step 512 that follow from predictions madeat 510 and the tasks entailed in carrying out the recommended actions,at steps 522 or 524, are distinct from each other.

After the selection of the recommended action has taken place, eitherautomatically or manually, the chosen recommended action is stored intothe database. In some embodiments, the outcomes resulting from the tasksperformed at steps 522 or 524 (the tasks performed to carry out thechosen recommended actions) are also stored at step 526 when the process500 obtains them. The user may enter outcomes if they are not obtainableby the prediction support system itself. The server executing theprocess 500 may retrieve the outcome automatically using either thereal-tome monitoring interface 222 or the input data translationcomponent 226 shown in FIG. 2 .

For actions that can be performed by the server executing the process500, an action library 546 contains the necessary script commands toperform the tasks needed to carry out the chosen recommended actions.The script commands contain the necessary instructions to perform theneeded task(s) whether it is performed at step 522 (automatic) or step524 (manual).

It should be noted that selecting a recommended action to be performedmay not actually cause the corresponding tasks to be performed. In acase where the client device or the server running the process 500 canperform the task, the task can be performed. However, if the recommendedaction requires the user to physically (or mentally) perform the task,then there is no guarantee that the user will actually perform it.

It should be noted that any of the steps and/or libraries in FIG. 4 maybe rearranged or combined with one or more other steps without changingthe function of the process 500. Steps 510, 512 and 514 for example canbe combined in a way to determine recommended actions based onhistorical outcomes and the actions taken historically that led to thoseoutcomes. One method of performing these steps starts by identifying aset of past scenarios or chains of events that closely relate to thecurrent scenario 505 (e.g., scenarios identified by the weightedcorrelation process performed at step 504 and/or the fuzzy ruleprocessing performed at step 506). The identified past scenarios can bestored as chain of event templates, where the chain of event templateseach include the historical values of the variables defining thescenario, the action that was taken and the resulting outcome. In oneexample, the recommended action can be determined by choosing thehistoric action that led to a desired or most favorable outcome mostoften. In another example, fuzzy logic can be used to combine multipleactions taken with the desirability of the resulting outcome where thefuzzy logic algorithms provide weighting to the desirability and thetype of actions taken. The chain of event templates may also include theaction that was recommended (if the scenarios were using the decisionsupport process) in order for the process 500 to be able to identifycases where the recommended action does not correspond to the actionthat was taken. This may avoid false feedback where a recommended actionwas ignored and a negative outcome resulted.

New chain of event templates are created when no past chain of eventscenarios match the current scenario 505. Existing chain of eventtemplates can also evolve to be substantially different from when theywere first created. This can be the result of parameters outside of thecontrol of the user or the process 500 evolving to create asubstantially different set of rules that control the chain of eventsscenarios that are represented by the templates.

In some embodiments, the application models developed using the process400 and executed with the process 500 can be structured to resemble agoal-seeking system that begins the process by querying the user for thedesired outcome. The process then obtains the values of the variables inthe current scenario 505 and compares those values to the variablesconsistent with the values of the desired outcome. The process 500described above then seeks to find scenarios (chains of events) thatwill identify actions to be taken to transition the current scenariotoward the desired scenario. There will typically be multiple paths toget to the desired result. The application model can query the user onhow he would prefer to get to the goal. The goal can be achieved whileoptimizing certain characteristics or variables of the applicationspecific model. For example, in an investment application, the goal maybe to double the value of an investment in 5 years. The decision supportprocess can identify multiple recommended actions (e.g., investing inhighly aggressive or conservative investments) that are chosen tominimize risk, maximize gain, provide less than a certain thresholdchance of losing all of the initial investment. In some embodiments, thegoal can be open-ended or indefinite. The goal may be to optimize asubjective quality. For example, the goal may be to arrive at adestination in the fastest time, in the least dangerous fashion, in themost comfort, etc. In these situations, fuzzy logic may be used toidentify the chain of events that best meet the chances of arriving atthe desired destination while optimizing the subjective quality.

Special consideration to a person's privacy (or other privateinformation such as top secret materials) may be a desirable feature forsome applications of the decision control process 500. For example, aperson's private information can be stored such that the private and/orsensitive information is made available for analysis and identificationof correlated scenarios that match a current scenario, but at the sametime keep the person's identity separated from the private and/orsensitive information. FIG. 9 is a flowchart illustrating certain stepsin a process for parsing private information and storing it insegregated databases, where the databases can be accessed in ananonymous mode to retain personal privacy, or accessed in an authorizedmode that reconstructs the private information including the personalidentity.

The process 900 obtains private records 905 containing a personalidentity and information concerning various facts about the person. Theinformation may include sensitive or private information that a personhas the right to keep private unless mitigating circumstances exist.Sensitive information may include financial information, criminalrecords, a record of associations or acquaintances, social securitynumber etc. The information by itself is not an issue. It is only whenthe information is linked with an identity of an individual that privacybecomes a concern. For this reason, when private information is importedto the decision support system (e.g., retrieved by the real-timemonitoring interface component 222 and or the import data translationcomponent 226 of the server 105 shown in FIG. 2 ), a private importprocess 910 is performed. The private import process 910 starts byparsing the incoming data. The data is parsed to a level needed to keepthe identity separate from sensitive or private information. A uniquecode is assigned to link the identity to the parsed information.Identities and codes may be stored in separate, secure databases withrestricted access. True identities are only revealed where specialconditions are met, such as securing a warrant. Warrants may berepresented as electronic keys to streamline automated processingwithout compromising security. After parsing, the information is storedinto any of several separated databases such as the databases 915A, 915Band 915C. The databases 915A to 915C can be stored on any kind of memorydevice such as hard drives, CD-ROM, magnetic tape, etc. The informationis stored in a data structure that links the unique code with theinformation.

The information in the individual databases can be retrieved (e.g., inorder to perform correlative analysis, making predicted outcomes, makingrecommendation of the process 500) separately by proceeding to block920. The separate databases do not contain enough information to linkthe person with the individual facts or events. Data from multipledatabases may be combined in a restricted way such that the chains ofevents used in the decision support process 500 can be identifiedwithout connecting a personal identity to the chain of events. Thisallows the data-mining or exportation of anonymous data.

In certain critical situations, such as when a person's safety isinvolved, or in cases where a criminal act has been committed, thereconstruction of the private records 905 may be permitted. Such asituation is similar to the issue of a warrant or a subpoena. Anelectronic warrant key is used to authorize, authenticate and/or permitdecryption of information (e.g., access codes or cryptographic keys) toallow reconstruction of the private record for which the authorizationwas granted 905. The authorization/authentication act is typically arecorded transaction that is kept in a secure database for purposes ofproviding an audit trail. When the electronic warrant 930 is obtained,the process performs step 925 where authentication of the warrant 930 isperformed and the subsequent reconstruction results in reconstructedrecords 935. Step 925 is able to access multiple data bases as well aslink the data in each database to the identification of the person thatit is linked to. Obtaining the reconstructed records 925 that includethe personal identity variable enables the use of the identity of theperson as a variable in the decision support process 500. It should benoted that any of the steps and/or libraries in FIG. 9 may be rearrangedor combined with one or more other steps without changing the functionof the process 900. While the process 900 was described in relation toparsing information to keep the information separate from a person'sidentity, those of skill in the art will recognize that other forms ofsensitive information can be protected in a similar manner. For example,the authorization warrant methodology can also be used to restrictaccess to other forms of private information. For example, governmentaltop secret information may be accessed only by authorized individuals.Similar protection may be afforded sensitive information such as companytrade secrets, secret negotiations, etc.

FIG. 10 is a flowchart illustrating certain steps in a process forobtaining information for use in the decision support process. Theprocess 1000 can be performed by the server based system 200 shown inFIG. 2 . Preferably, any information that is retrieved by the system 200performs all of the steps shown in the process 1000. However, some stepsmay be omitted without changing the function of the process 1000. Theinformation may be retrieved by the real-time monitoring interfacecomponent 222 and/or the import data transaction component 226. Theprocess 1000 starts with an authentication process 1005. Theauthentication process verifies the source of the retrieved information.The authentication process confirms that the received data is unchangedfrom what the source transmitted and it also confirms the identity ofthe source. In some embodiments, the sender uses a one-way hash functionto generate a digital signature from the transmitted data. The senderthen encrypts the hash-code with a private key. The receiver (e.g. theserver based system 200) then recalculates the digital signature fromthe data and decrypts the received digital signature with the sender'spublic key. If the two digital signatures are equal, the receiver can beconfident that data has not been corrupted and that it came from thegiven sender. The integrity services module 242 may perform theauthentication at step 1005.

By proceeding to decision block 1010, the process 1000 checks if theauthentication was successful. If the authentication was not successful,the process 1000 is aborted and the retrieved information may bediscarded. The retrieved information may also be retained to be usedlater.

If the authentication was successful, the decision block 1010 directsthe process 1000 to proceed to a decryption process 1015. The decryptionprocess 1015 decrypts the information. The integrity of the decrypteddata is typically verified through the use of checksums (e.g., cyclicredundancy checks). If portions of the decrypted data are determined tobe erroneous, decision block 1020 will determine that the decryption wasunsuccessful for that portion of data and abort the process (discard theerroneous portions of data) as it pertains to the erroneous data. Theintegrity services module 222 may perform the decryption at step 1015.

After successful decryption of the information, the decision block 1020directs the process 1000 to a decompression process 1025. Any of thevarious compression/decompression algorithms can be used to compressinformation depending on the type of media that is being compressed. Ifdecompression is unsuccessful, the decision block 1030 causes theprocess 1000 to be aborted, at least for the portion of information thatwas unsuccessfully decompressed. The integrity services module 222 mayperform the decompression at step 1025.

After successful decompression of the information, the process 1000proceeds to a filtering process 1035. The filtering process can performthe reformatting tasks discussed above in relation to the import datatranslation module 226. The data is filtered so as to conform to theformat desired by the decision support system 200. If the filtering atstep 1035 is determined, at decision block 1040, to be unsuccessful intranslating the information into a conformal format, then the process1000 is aborted, at least for the portion of information that was notsuccessfully filtered.

After successful filtering of the information, the process 1000 proceedsto step 1045 where the privacy process 900 shown in FIG. 9 is performed.The privacy process 1045 can be performed in at least two exemplaryways. In one example, the privacy process 1045 is used to reconstructthe private information (see the reconstructed records 935 in FIG. 9 )retrieved from the already parsed databases (e.g., the databases 915A,915B and 915C). In this case, decision block 1050 checks to see if theproper authorization warrant 930 is available. If the authorizationwarrant 930 is not available, the decision block 1050 aborts the process1000 as it relates to obtaining the reconstructed records 935. If theprivate information is determined to be authorized to be obtained, thenthe decision block 1050 directs the process 1000 to a parsing process1055. In a second example, the privacy process 1045 is used to determineif there is private information contained in the retrieved informationthat needs to be parsed before the data is stored for use in thedecision support system. In this case, the decision block 1050determines if the private information (e.g., personal identityinformation) has been identified (e.g., flagged) to be parsed into thesecure personal identification/unique code database that was discussedabove in relation to FIG. 9 . This way the parsing process 1055 canrecognize that there is personal identity information present and theunique code can be linked to the parsed information to separate theinformation from the personal identity. If the personal identityinformation is properly identified, then the process 1000 proceeds tothe parsing process 1055.

The parsing process 1055 performs parsing of the received informationinto the various variables that are used to identify related chains ofevents in the process 500 discussed above. At step 1060, the parsedinformation is then linked to classifications of the data structure tobe used for the decision support process. The linking can be in the formof cross references to the various application specific model templatesthat the information may be relevant to. The linking can be in the formof cross references to the individual chain of events templates used indetermining the predicted outcomes and the recommended actions of theprocess 500. The linking can also be in the form of cross references tothe various libraries used in the process 400 for developing theapplication specific models.

After linking the parsed information, the process 100 proceeds to step1065, where the information is stored into the multidimensional datastructure to be used in the processes discussed above. It should benoted that any of the steps in FIG. 10 may be rearranged or combinedwith one or more other steps without changing the function of theprocess 1000.

An exemplary use of some of the features of the decision support systemdiscussed above will now be described. This example describes a scenariofor monitoring a shipping container. History of accident or disasterinvestigation has shown us that connections between seemingly unrelatedor insignificant events play a major part in the failure of the entiresafety system. One such example of the nature of humans to mistake veryvital clues and relationships of key factors in a disaster was the crashof Alaska Airlines flight 261 Jan. 31, 2000 that killed all 88 peopleaboard the aircraft.

The initial investigation of the flight records and aircraft wreckageshowed significant signs pointing to mechanical failure. Specifically,the National Transportation Safety Board concluded that a flight controlsystem inside the horizontal stabilizer called a jackscrew and gimbalnut assembly failed during flight in the tail of the MD-83. Analystsconcluded that the jackscrew had torn through the badly worn nut causingthe horizontal stabilizer, the flight control that regulates the pitchof the nose, had jammed in the full nose down position causing theaircraft to dive into the ocean.

More than six years after the accident there has still not been advancesin the sensor technology on aircraft that would have warned the pilotsthat the jackscrew would be under enough stress to strip the gimbal nutassembly causing a catastrophic failure.

Conversely, over the same six year period, data gathering, data mining,decision tree analysis, neural networks, and threat mitigationalgorithms have enjoyed great increases in sophistication. This path ofcontinued improvement has led to the present invention. The following isan example of how the present invention would have illustrated the fullextent of the risk associated with flight 261 and how the flight crewand public would have been presented with the risks involved with AlaskaAirlines as a corporation, and with the entire US airline industry atthe time.

Jan. 31, 2000 0900-PST

The pilot arrives at the aircraft to perform the routine preflightchecks of the aircraft. As the system powers up, the Trusted DecisionSupport System has pre-assigned the subject being analyzed as aircraftsafety of flight. The decision support system references the modellibrary and selects the script designated for the aviation safety model.

The model for aviation safety identifies areas specific to aviationsafety that must be analyzed by the system. For example, one aspect ofthe model instructs the system to reference the FAA Airmen CertificationDatabase using the pilot information for the crew assigned to flight261. The airmen certificate number, the pilot name, and the airlineemployee identification number are used to research the pilot flighttest records, the pilot citation records, pilot citation records, pilottraining records, pilot conduct records, and pilot performance records,aircraft flown records, and type rating records. The decision supportsystem then references the scenario library while conducting acorrelation process between the information in the library and theinformation provided about the flight crew. The rule library isreferenced for allowable parameter values and a customized set of rulesregarding the flight crew is established for the flight. The value ofparameters are evaluated against the fuzzy rule set to determine if anyvalue is outside the established allowable values. For example, rulesfor this flight based on the historical values given from the scenariolibrary dictate that the crew must have 10 hours of off duty rest periodbetween block times in the aircraft. The pilot information databaseconfirms 11.5 hours between the aircraft shut down, the crew arrival,and aircraft restart. This variable would be evaluated as a non-anomalybased on it falling within the parameters allowed by the establishedrule set.

The process of establishing rule sets and evaluating parameter values isperformed throughout all the requirements of the aviation safety model.

The aviation safety model used in this scenario calls for themaintenance records of the specific MD-83 aircraft scheduled for flight261 to be referenced. The rule library for this scenario dictates thatthe variable may not have any instances of required maintenance workgoing undone. The referenced maintenance records indicate that inSeptember 1997 mechanics at the Alaska Airlines Oakland maintenancefacility discovered that the gimbal nut on the horizontal stabilizer was“badly worn and in dire need of replacement.” The same maintenancerecords showed that Alaska Airlines ordered additional tests on theassembly and deemed it airworthy with no maintenance action required.The rule set for this scenario calls for no deviations from requiredmaintenance actions. This event would be flagged as negatively affectingthe safety of flight for this aircraft

Data collected from grand jury investigations showed that AlaskaAirlines had allowed more that 840 flights by two MD-80 series jetliners“in an unairworthy condition” between October 1998 and January 1999. Therule set for the Aviation Safety model states that no unairworthyaircraft are permitted to fly at any time. The information from theinvestigation would be flagged by the system as negatively affecting thesafety of flight.

According to the aviation safety model, records from the FBI would bereferenced in the correlation and data gathering process. The FBIrecords show that in December of 1998 the Alaska Airlines Corporateheadquarters in Seattle were raided based on charges filed by thePresident of the Local Machinists Union and senior aircraft mechanicJohn Liotine that “supervisors and mechanics were signing off work thatthey were either unqualified to do, or had not performed, so that planescould be put back into service as soon as possible.” The rules for thisvariable state that any investigation by the FBI as well as anyaccusations by current or former employees are labeled as a negativeinfluence on the safety of flight.

In February 1999 the FAA database for airline citations shows thatAlaska Airlines allowed aircraft to fly despite falsified maintenancechecks that included work by an Alaska Airlines supervisor who was notqualified to perform such work. According to the rules for thisvariable, one airline citation is outside the allowable value range forthe variable. This variable would be flagged as negatively affecting thesafety of flight.

The system references a function library to conduct multiple levels ofanalysis regarding the information provided in the correlation processand the variable rule processing stages.

The system references a prediction library to base probabilities ofevent occurrence based on current variable values. The system referencesthe time when the pilot activates the electrical system of the aircraftas the starting point to reference the flight timeline against. Allpredictions made for this flight from the prediction library are linkedto a predicted time of occurrence related to this timeline.

Jan. 31, 2001 0901-PST

Each event is linked to a list of consequences related to thatrecommendation via the consequence library.

Jan. 31, 2001 0902-PST

The pilot is presented a score based on the risk factors for the flight,and given a list of events and their corresponding probability score.Each event listed is given a time of occurrence and is linked to a listof recommendations from the recommendation library.

Jan. 31, 2001 0903-PST

The crew is presented with a list of actions, both manual and automaticfor them to choose from to lessen the risk associated with this flight.

Jan. 31, 2001 0915-PST

The crew has performed all actions listed on the actions page andpermitted the automated systems to perform all available actionsautomatically.

Jan. 31, 2001 0916-PST

The actions of the crew, and the results of the actions are stored inthe database for the flight and the process is reset automaticallyupdating with each change in any observed variable in the aviationsafety model.

Had the flight crew seen the risk report and list of probable failuresand the likelihood of those failures occurring, they may not have chosento make the flight. However, for the example, it is assumed that theflight has occurred.

Inside the flight deck the system constantly updates the informationprovided by the prediction system, along with readings from the avionicssystems aboard the aircraft. Current conditions and flight systeminformation is run through the fuzzy rule process based on informationfrom the rule library. Each variable is assessed through the fuzzy ruleset to determine whether its value falls within the allowable parametervalue limits. For example, the control pressure for pitch attitude has alimit of 100 pounds of force required at the control input end tomanipulate the control. The system was on autopilot, so no control inputshould have been taking place. However, due to the malfunction of thepitch attitude control mechanism, constant input from the trim systemwas required to maintain aircraft attitude.

Jan. 31, 2001 1230-PST

The pitch trim force required variable observed to be within theallowable fuzzy rule parameters. The analysis of the system, and theprediction process show that according to the current patterns, thepitch trim force required to maintain level flight will exceed theallowable parameter value limits in one hour and 45 minutes of flight.

Jan. 31, 2001 1231-PST

The system determines the recommended actions according to the timelineestablished by the prediction process. The consequence library is usedto determine potential consequences for each action performed by thecrew, automated systems, and failures of systems and theirinterdependent systems.

Jan. 31, 2001 1232-PST

The crew is alerted about the results of the process and prompted toacknowledge the severity of the consequences of the predicted systemfailures. Both manual and automatic actions are performed to mitigatethe risks associated with the failure.

Jan. 31, 2001 1232-PST

The FAA, emergency crews at the nearest airports, search and rescuepersonnel, aircraft experts, airline emergency maintenance personnel,and airline crisis management teams are all notified of the situation.Each entity is updated with complete information automatically relayedto and from the aircraft. The timeline for failure is constantly updatedwith each action taken by the flight and ground crews.

The system has alerted the pilots and all appropriate parties of theimpending failure and its consequences 1:56 minutes prior to the actualpoint of failure. Based on the actions recommended by the decisionsupport system, the crew is able to adjust the flight controls on theaircraft to minimize the forces acting upon the horizontal stabilizer.Furthermore, the aircraft is diverted to the nearest location, landingsafely in San Diego, 42 minutes prior to the point of failure.

While the above detailed description has shown, described, and pointedout novel features of the invention as applied to various embodiments,it will be understood that various omissions, substitutions, and changesin the form and details of the device or process illustrated may be madeby those skilled in the art without departing from the spirit of theinvention. As will be recognized, the present invention may be embodiedwithin a form that does not provide all of the features and benefits setforth herein, as some features may be used or practiced separately fromothers.

What is claimed is:
 1. A method comprising: selecting, by an electronicdevice comprising at least one processor, a data set for an applicationarea; creating, by the electronic device comprising at least oneprocessor, a weighted data set based on weighted scores assigned to datain the data set; generating, by the electronic device comprising atleast one processor, a correlation between the weighted data set and oneor more previously correlated weighted data sets; and determining, bythe electronic device comprising at least one processor, a recommendedaction as a response to an event related to the data set, and outcomeinformation for the recommended action, wherein said determining isbased at least in part upon the correlation between the weighted dataset and one or more previously correlated weighted data sets.
 2. Themethod of claim 1, further comprising performing, by the electronicdevice comprising at least one processor, statistical analysis on thedata set.
 3. The method of claim 1, wherein the weighted data set iscreated according to weighting guidelines that evolve and develop overtime.
 4. The method of claim 1, further comprising performingstatistical analysis on the data set, wherein statistical analysisperformed on the data set is performed by a statistical analyses engineprogrammed with one or more models, wherein each model comprisesmathematical instructions for processing the data set.
 5. The method ofclaim 4, wherein the mathematical instructions comprise weights to beassigned to the data set according to at least one of a source fromwhich the data set was received and an age of the data set.
 6. Themethod of claim 4, wherein the mathematical instructions comprise atleast one of fuzzy logic instructions, Bayesian analyses instructions,neural network analyses instructions, probability calculationinstructions, mean calculation instructions, confidence intervalcalculation instructions, Z-test instructions, T-test instructions,autoregressive modeling instructions, or residual analysis instructionsfor multiple regression.
 7. The method of claim 1 wherein the data setfor an application area comprises data from a sensor network.
 8. Themethod of claim 7, further comprising performing, by the electronicdevice comprising at least one processor, statistical analysis by astatistical analyses engine on the data set.
 9. The method of claim 7further comprising performing statistical analysis on the data set,wherein the statistical analyses engine is configured to be programmedwith one or more models, wherein each model comprises mathematicalinstructions for processing the data set.
 10. The method of claim 9,wherein the mathematical instructions comprise weights to be assigned tothe data set according to at least one of a source from which the dataset was received or an age of the data set.
 11. The method of claim 9,wherein the mathematical instructions comprise at least one of fuzzylogic instructions, Bayesian analyses instructions, neural networkanalyses instructions, probability calculation instructions, meancalculation instructions, confidence interval calculation instructions,Z-test instructions, T-test instructions, autoregressive modelinginstructions, or residual analysis instructions for multiple regression.12. A method comprising: selecting, by an electronic device comprisingat least one processor, a data set for an application area; analyzing,by the electronic device comprising at least one processor, the data setbased on fuzzy logic instructions; generating, by the electronic devicecomprising at least one processor, a recommended action and outcomeinformation for the recommended action; and performing, by theelectronic device comprising at least one processor, statisticalanalysis by a statistical analysis engine configured to be programmedwith one or more models, wherein each model comprises mathematicalinstructions for processing the data set, wherein the mathematicalinstructions comprise weights to be assigned to the data set accordingto at least one of a source from which the data set was received or anage of the data set.
 13. The method of claim 12, wherein themathematical instructions comprise at least one of fuzzy logicinstructions, Bayesian analyses instructions, neural network analysesinstructions, probability calculation instructions, mean calculationinstructions, confidence interval calculation instructions, Z-testinstructions, T-test instructions, autoregressive modeling instructions,or residual analysis instructions for multiple regression.
 14. Themethod of claim 12, further comprising: receiving, by the electronicdevice comprising at least one processor, current data from at least oneof a plurality of sources; comparing, by the electronic devicecomprising at least one processor, the current data and the previouslyreceived data; and providing, by the electronic device comprising atleast one processor, a recommended action and outcome information forthe recommended action based at least in part on the comparison.
 15. Themethod of claim 14, wherein the plurality of sources includes disparatesources.
 16. The method of claim 14, further comprising a database ofpreviously recommended actions associated with the previously receiveddata, wherein the electronic device is further configured to provide arecommended action and outcome information based at least in part on thecurrent data, the previously received data, or the associated previouslyrecommended actions.
 17. A method for performing an application specificdecision support model comprising: identifying, by an electronic devicecomprising at least one processor, data sources for an application area;selecting, by the electronic device comprising at least one processor,variables to be searched for in each identified data source; assigning,by the electronic device comprising at least one processor, weights toeach variable searched, wherein weights correspond to relevance ofinformation in each data source; identifying, by the electronic devicecomprising at least one processor, instructions to apply to a search ofa selected variable in an identified data source; conducting, by theelectronic device comprising at least one processor, a correlationprocess, wherein a current scenario is correlated with previousscenarios; conducting, by the electronic device comprising at least oneprocessor, multiple analysis on the current scenario; determining, bythe electronic device comprising at least one processor, at least onenext likely outcome or event for at least one time point; identifying,by the electronic device comprising at least one processor, at least onerecommendation for each time point, wherein the at least onerecommendation is based on the at least one next likely outcome orevent; determining, by the electronic device comprising at least oneprocessor, at least one potential consequence for each recommendation;performing, by the electronic device comprising at least one processor,an action based upon the at least one recommendation and at least onepotential consequence; and storing, by the electronic device comprisingat least one processor, results of the action.
 18. The method of claim17, wherein the making of at least one prediction, the identifying of atleast one recommendation, or the determining of at least one potentialconsequence are executed simultaneously.
 19. The method of claim 17,wherein the instructions comprise at least one of fuzzy logicinstructions, Bayesian analyses instructions, neural network analysesinstructions, probability calculation instructions, mean calculationinstructions, confidence interval calculation instructions, Z-testinstructions, T-test instructions, autoregressive modeling instructions,or residual analysis instructions for multiple regression.
 20. Themethod of claim 17, wherein the action performed comprises presentingthe at least one recommendation and at least one consequence to a userfor a user input.